Understanding Preconception Women’s Needs and Preferences for Digital Health Resources: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Improving preconception health can benefit all women, their children, and their families regardless of their individual pregnancy intentions. Rapidly increasing access to information technology and online engagement have created opportunities to use digital health resources to engage with preconception women regarding lifestyle behaviors. OBJECTIVE: This study explores how preconception women engage with digital health resources and online platforms to inform the design and development of a digital health resource to support women to make positive behavior change for their preconception health. METHODS: This codesign research followed the Double Diamond process, which focuses on contextualization and explorative processes in phase 1 and ideation and development processes in phase 2. Phase 1 is reported on in this study and was undertaken via a series of 1-on-1 in-depth interviews with female participants (N=12) aged 18-45 years over 3 months. Interviews were designed to explore participants' lived experiences in relation to their health and desired supports for healthy lifestyle behaviors. The first interview focused on participants' perceptions of health and health behaviors, the second interview focused on social connections for health, and the third interview focused on digital health information and supports. Conversations from the first interview informed the development of the second interview, and conversations from the second interview informed the development of the third interview. Community advisors (N=8) met to provide feedback and advice to the researchers throughout the interview process. Qualitative analyses of transcripts from interviews were undertaken by 2 researchers before a deductive process identified themes mapped to the capability, opportunity, motivation, and behavior (COM-B) framework. RESULTS: In total, 9 themes and 8 subthemes were identified from 124 codes. In relation to digital health resources, specifically, participants were already engaging with a range of digital health resources and had high expectations of these. Digital health resources needed to be easy to access, make women's busy lives easier, be evidence based, and be reputable. Social connectedness was also highly important to our participants, with information and advice from peers with similar experiences being preferred over yet more online health information. Online communities facilitated these social interactions. Participants were open to the idea of chatbots and virtual assistants but acknowledged that they would not replace authentic social interactions. CONCLUSIONS: Codesigned digital health resources should be evidence based, reputable, and easy to access. Social connections were considered highly important to women, and designers of digital health resources should consider how they can increase opportunities for women to connect and learn from each other to promote health behaviors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".